#227 Mixfits (3-14)

avg: 333.44  •  sd: 56.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
248 Mixed on the Rock Win 13-6 393.91 Jul 21st The Royal Experience 18
191 Coalition Ultimate Loss 9-12 274.31 Jul 21st The Royal Experience 18
234 rubber duck ultimate. Win 12-9 601.51 Jul 21st The Royal Experience 18
135 Blitzkrieg Loss 10-11 832.73 Jul 21st The Royal Experience 18
128 Boomtown** Loss 4-15 377.74 Ignored Jul 22nd The Royal Experience 18
191 Coalition Ultimate Loss 9-15 104.2 Jul 22nd The Royal Experience 18
204 Spring Creek Ascension Loss 8-9 421.53 Aug 18th Riverside Classic 2018
97 tHUMP** Loss 5-15 539.88 Ignored Aug 18th Riverside Classic 2018
194 Freetail Loss 11-14 294.27 Aug 18th Riverside Classic 2018
64 Sellout** Loss 3-15 688.24 Ignored Aug 19th Riverside Classic 2018
178 Balloon Loss 7-9 408.96 Aug 19th Riverside Classic 2018
97 tHUMP** Loss 3-11 539.88 Ignored Sep 8th Texas Mixed Sectional Championship 2018
49 Cosa Nostra** Loss 3-11 771.79 Ignored Sep 8th Texas Mixed Sectional Championship 2018
194 Freetail Loss 6-7 482.61 Sep 8th Texas Mixed Sectional Championship 2018
221 Chili Poppers Loss 5-10 -177.24 Sep 9th Texas Mixed Sectional Championship 2018
- boom shaka laka Win 7-3 333.44 Sep 9th Texas Mixed Sectional Championship 2018
167 Wildstyle Loss 4-11 183.29 Sep 9th Texas Mixed Sectional Championship 2018
**Blowout Eligible

FAQ

The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)